johnson slides 2020 final · horizon scanning using machine learning refugee flows and instability*...

Future Crime

Professor Shane D JohnsonDawes Centre for Future Crime at UCL

Dawes Centre for Future Crime at UCL

Office for National Statistics

Dawes Centre for Future Crime at UCL

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Dec

87

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93

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97

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Exc Fraud andComputer Misuse

Inc Fraud andComputer Misuse

Office for National Statistics

Dawes Centre for Future Crime at UCL

0.00

5000.00

10000.00

15000.00

20000.00

25000.00

Dec

81

Dec

87

Dec

93

Dec

97

Mar

02

Mar

04

Mar

06

Mar

08

Mar

10

Mar

12

Mar

14

Mar

16

Jun

18

Thou

sand

s of o

ffenc

esAll CSEW crime

Exc Fraud andComputer Misuse

Inc Fraud andComputer Misuse

Dawes Centre for Future Crime at UCL

A classic Crime Harvest and Retrofit SolutionDawes Centre for Future Crime at UCL

There were only two permutations!

+ = Crime opportunity

+ = No crime opportunity

Future Crime

• How do we avoid crime harvests?

• There are no “future facts” Jouvenel (1967)

• Challenge of methods for futures….

– Horizon scanning– Delphi– Stated preferences– Penn testing– SRs

• Where to start?

Dawes Centre for Future Crime at UCL

Addressing Online CSE on Social Media

Crime, place and the internet

The effects of cyber weapons

Smart Cities: Opportunities for crime prevention

Cybercrime risks to future street infrastructure

Guarding against Adversarial perturbation

Biocrime

Detecting emerging crime in

online markets

Horizon Scanning using Machine

Learning

Refugee flows and instability*

Current Research Themes

Terrorist financing and money laundering

Crime, place and the internet Cryptocurrency &

Fraud

Advanced materials to

combat crime

Recent and future trends in

counterfeit goods

Developing technologies

IoT and Crime

Refugee flows and instability*

Scoping projects

Longer projects

PhD projects

Dawes Centre for Future Crime at UCL

Ageing and online fraud

AI and Crime

Reducing domestic abuse with technology

Consumer Internet of Things (IoT)

Source: Intel

Dawes Centre for Future Crime at UCL

Consumer IoT

Race to market + No security regulation + Cost = ?

Dawes Centre for Future Crime at UCL

Systematic Review of Crime Facilitated by IoT

Initial Search3506 Initial

Screen2708Full

Screen198 Coded114

Databases Searched: Web of Science, ProQuest, ACM Digital Library, IEEE Xplore Digital Library, and Scopus

Dawes Centre for Future Crime at UCL

Blythe, J. M., & Johnson, S. D. (2019). A systematic review of crime facilitated by the consumer Internet of Things. Security Journal, 1-29.

Thematic Synthesis

Dawes Centre for Future Crime at UCL

Methods of Attack

Method Description

Denial of Service (DoS) Services disrupted through > requests from single devicesDDoS DoS attacks from multiple sources (e.g. botnets)Malware Malicious s/ware to compromise device and change functionsMan-in-the-Middle Interception of data in transitPhysical attacks Attacker physically tampers with deviceData integrity Attacker compromises data — inserting, modifying, replay attackSpoofing Attacker masquerades as anotherSide channel Exploits seemingly non-sensitive info e.g. power usageUser impersonation Attacker impersonates user (e.g. social engineering attack)

Dawes Centre for Future Crime at UCL

Methodology & Plausability

Dawes Centre for Future Crime at UCL

Crimes/Harms

Dawes Centre for Future Crime at UCL

Dawes Centre for Future Crime at UCL

Open Source Search

• News media published in two 60-day periods: – 20 January 2018 to 21 March 2018, and – 14 May to 13 August 2019

• NLP screening

• Google searches (same period, 99 articles)

Initial Search64k NLP

Screen1.6k FullScreen287

Dawes Centre for Future Crime at UCL

Crimes/Harms

Dawes Centre for Future Crime at UCL

Secure by Design

• Warning signs of a crime harvest

• Heterogeneity – can you tell if a device is secure before you buy it?

Dawes Centre for Future Crime at UCL

Smart Cities

A city that uses information and communication technologies (ICTs) and all other technologies available to improve the effectiveness and efficiency of city services in order to save resources and improve citizens’ well-being

Ramaprasad et al. (2017)

Laufs, J., Borrion, H., and Bradford, B. (2020). Security and the smart city: A systematic review. Sustainable Cities and Society, 55

Dawes Centre for Future Crime at UCL

Smart Cities

Laufs, J., Borrion, H., and Bradford, B. (2020). Security and the smart city: A systematic review. Sustainable Cities and Society, 55

Dawes Centre for Future Crime at UCL

Laufs, J., Borrion, H., and Bradford, B. (2020). Security and the smart city: A systematic review. Sustainable Cities and Society, 55

Dawes Centre for Future Crime at UCL

Smart Cities

Laufs, J., Borrion, H., and Bradford, B. (2020). Security and the smart city: A systematic review. Sustainable Cities and Society, 55

Dawes Centre for Future Crime at UCL

Smart Cities

Laufs, J., Borrion, H., and Bradford, B. (2020). Security and the smart city: A systematic review. Sustainable Cities and Society, 55

Dawes Centre for Future Crime at UCL

Smart Cities

Laufs, J., Borrion, H., and Bradford, B. (2020). Security and the smart city: A systematic review. Sustainable Cities and Society, 55

Dawes Centre for Future Crime at UCL

Systematic Review – Systematic Approach?

• Crime/Security are often omitted from (Smart) city plans

• Needs a systematic approach - dependencies across (silo) systems and sectors

• Governance:– Who owns the data?– Balancing collective security and individual privacy?

Dawes Centre for Future Crime at UCL

Artificial Intelligence

Dawes Centre for Future Crime at UCL

Machines are learning to find concealed weapons in X-ray scansCOMPASS@CS_UCL

https://www.economist.com/news/science-and-technology/21711016-artificial-intelligence-moves-security-scanning-machines-are-learning-find

http://www.economist.com/news/science-and-technology/21711000-week-how-optical-navigation-can-help-bomb-find-its-target-without-gps

Autonomous software

AI to prevent crime

Dawes Centre for Future Crime at UCL

Adversarial Perturbations (speech to text)

Carlini, N., and Wagner, D. (2018). Audio Adversarial Examples: Targeted Attacks on Speech to Text. Deep Learning and Security Workshop.

Dawes Centre for Future Crime at UCL

Google AI thinks rifle is a helicopter (Can you spot the difference?)Adversarial examples are easier to create than previously understood

MIT team reliably fooled Google’s Cloud Vision API, a machine learning algorithm used in the real world today.

http://www.labsix.org/physical-objects-that-fool-neural-nets/https://www.wired.com/story/researcher-fooled-a-google-ai-into-thinking-a-rifle-was-a-helicopter/

Defeat to AI

AI to prevent crime

AI to commit crime

Adversarial perturbations

Dawes Centre for Future Crime at UCL

Generative Adversarial Networks (GANs)

https://forms.gle/9vD5hHHKNtc7y5p3A

Dawes Centre for Future Crime at UCL

Dawes Centre for Future Crime at UCL

AI and Crime

Dawes Centre for Future Crime at UCL

Dawes Centre for Future Crime at UCL

Biotechnology

• Traditional biological systems are re-created or modified in novel ways for various application purposes

• CRISPR, Gene drives, DNA testing

• In 2018, £2.2bn was raised by biotech companies in the UK

Dawes Centre for Future Crime at UCL

Synthetic Biology

• Biohacking - Athletes have historically abused steroids and growth hormones (Reardon & Creado, 2014)

• 23AndMe data sharing - informed consent?

• DNA encoded malware (Ney et al., 2017)

Dawes Centre for Future Crime at UCL

“Now that DNA sequencing, synthesis, manipulation, and storage are increasingly digitized, there are more ways than ever for nefarious agents both inside and outside of the community to compromise security.”

Peccoud et al 2017

Dawes Centre for Future Crime at UCL

Addressing Online CSE on Social Media

Crime, place and the internet

The effects of cyber weapons

Smart Cities: Opportunities for crime prevention

Cybercrime risks to future street infrastructure

Guarding against Adversarial perturbation

Biocrime

Detecting emerging crime in

online markets

Horizon Scanning using Machine

Learning

Refugee flows and instability*

Current Research Themes

Terrorist financing and money laundering

Crime, place and the internet Cryptocurrency &

Fraud

Advanced materials to

combat crime

Recent and future trends in

counterfeit goods

Developing technologies

IoT and Crime

Refugee flows and instability*

Scoping projects

Longer projects

PhD projects

Dawes Centre for Future Crime at UCL

Ageing and online fraud

AI and Crime

Reducing domestic abuse with technology

And finally

• Innovation-crime sequences

• Multidisciplinarity (AI, synbio, chemistry, sensors, cyber, ……)

• Multi-stakeholder (Gov, police, voluntary sector, industry, ……)